Iran Credit Scoring
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Our Road:
What we are eager to learn, boils up when we find a missing puzzle in our mindset.
We will recognize these pieces while we keep chasing our curiosity for a while, which is not common among many people.
We are modelling an Explainable AI for credit scores in finance technology (FinTech).
creating a pipeline for gathering and cleaning data and then tunning some models to teach machines assess individuals and firms in a fair and precise way, is our main challenge.
In this amazing mission, we also need to create a communication plan between our AI, Our CRM and our Marketing team to make our society grow with the help of our years old AI.
During recent years, technology has been developed to the point where we can converse with LLM models like ChatGPT.
The significant advancement here can be seen as a new way of interacting with computers, prompting them as we have historically done when giving commands to people.
Creating an Explainable AI (XAI) model involves several key steps in the modeling pipeline. Here's a detailed breakdown of the pipeline:
1. Problem Definition
Identify Objectives: Define what problems the AI model aims to solve.
Stakeholder Involvement: Engage stakeholders to understand their needs for transparency and interpretability.
2. Data Collection
Data Sources: Gather data from various sources, ensuring it is relevant to the problem.
Data Quality: Assess the quality of data, checking for missing values, biases, and inconsistencies.
3. Data Preprocessing
Cleaning: Remove or correct erroneous data points.
Normalization/Standardization: Scale features to a uniform range to improve model performance.
Feature Engineering: Create new features that may help the model learn better.
4. Model Selection
Choose Algorithms: Select algorithms that are inherently interpretable (e.g., decision trees) or those that can be made interpretable (e.g., complex models with post-hoc explanation methods).
Consider Trade-offs: Balance between model complexity and interpretability based on the project requirements.
5. Model Training
Train the Model: Use the prepared dataset to train the model.
Hyperparameter Tuning Optimize model parameters to improve performance while ensuring interpretability.
6. Evaluation and Ethical Considerations
Performance Metrics: Evaluate the model using appropriate metrics (accuracy, precision, recall, F1 score).
Explainability Metrics: Assess the model's explainability through metrics like fidelity, stability, and comprehensibility.
Bias and Fairness: Evaluate the model for biases and ensure fairness in predictions.
Compliance: Ensure that the model adheres to relevant regulations and ethical guidelines regarding AI transparency.
7. Explainability Techniques
Local Explanation Methods: Use techniques like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) to explain individual predictions.
Global Explanation Methods: Analyze model behavior across the entire dataset using feature importance scores, partial dependence plots, or surrogate models.
8. Model Deployment
Integration: Deploy the model into the production environment.
User Interface: Develop user-friendly interfaces to present model predictions and explanations to end-users.
9. Monitoring and Maintenance
Performance Tracking: Continuously monitor model performance and explainability post-deployment.
Feedback Loop: Incorporate user feedback to refine explanations and improve model performance over time.
10. Documentation and Reporting
Documentation: Maintain thorough documentation of the model development process, including decisions made regarding explainability.
Reporting: Communicate findings and model behavior to stakeholders, ensuring transparency in how decisions are made.
Calculation Flow Diagram
